The second modeling step (Fig. REF b) is a normal
feed-forward network fed by the document vectors \(V_{d}\)  (pertaining
to the set of documents \(D\) ), which we train by stochastic gradient
descent and backpropagation [1]} to predict
distress events \(e\in \left\lbrace  0,1\right\rbrace \) . Hence, the objective is to maximize the average
log probability:
\(\frac{1}{|D|}\sum _{d\in D}\mathrm {log}\, p(e_{d}|V_{d})\) 
